It is fascinating how Blake gets it 100% right and 100% wrong at the same time.

He correctly identifies that the work-life dichotomy does not make sense (to a point of causing harm) for those of us for which work is not a necessary evil required for “paying the bills”, but a more profound vehicle for finding purpose and meaning in our lives.

He also correctly identifies that the different “parts” of our lives are intertwined, and our state-of-mind and our behavior act as a feedback loop, either a positive or a negative one, tying all of those activities together into one system. Therefore, activities that were traditionally bucketed into the “non-work” part of the equation: exercise, sleep, eating healthy, strong social connections (the latter is often overlooked) can have a profound positive impact on our work when they exist and a profound negative impact on our work when they don’t.

Where Blake goes from here is where his reasoning and mine diverge. Blake adopts work as his primary frame of reference, and rationalizes investing in the non-work activities through the positive impact that they have on work. By only acknowledging a one-way connection (from non-work to work), maintaining the dichotomy (in a sense) and ignoring the intrinsic impact of these activities, he is not taking his conclusion far enough and may end-up with a sub-optimal outcome, IMHO.

An alternative frame of reference would put purposeful and meaningful life front and center instead. It would recognize that all the activities and non-activities (sleep, meditation, etc.) that we choose to engage in, either contribute to it or subtract from it both directly and indirectly – through the impact they have on other activities. This “global optimization” problem is a harder problem to solve, and I haven’t found any short-cuts to solving it (yet). But it’s also what makes life fun and interesting.

He provides some phenomenal advice for companies to better engage their customers and build products that they love. But the parts that really resonated with me were the analogies he drew between the relationships companies have with their customer and “real life” personal relationship.

Specifically he equates the relationship with new users to dating and the one with existing users to marriage.

From the former, he draws the importance of first impressions. The first interactions with the customer are the ones that matter the most, and he extends the typical definition to apply to any “first”.

But it’s the latter that even more profound. He argues that couples typically fight around 5 major topics:

Money

Kids

Sex

Time

Others, as in “other people”: jealousy, in-laws, etc.

And customers typically call support about very similar topics:

Cost/Billing (Money)

Users’ clients (Kids)

Performance (Sex)

Roadmap (Time)

Others, as in other companies: competition, partnerships, etc. (Others)

The reason many relationships break up can be attributed to four negative behaviors:

Criticism – not focusing on the issue at hand, but on the over-arching issues (you never listen to your customers)

Contempt – intentionally trying to insult

Defensiveness – not taking accountability, making excuses

Stonewalling – shutting down, not feeling a need to answer/respond

And the same four behaviors apply to bad customer support.

He then make a pretty compelling case for why engineers and designers should be fielding customer support calls themselves. Which you can learn more about by watching his talk.

There are little to no network effects within each side of the market. The winner-takes-all dynamic is created when the two sides of the market interact:

Company X has the majority of riders (more demand) –> Drivers will increasingly serve Company X’s customers (more supply) –> Company X’s service level improves (more supply liquidity) –> Even more riders to choose Company X.

Just like in the CPG industry, given that purchases are habitual, prices are similar and the products are not highly differentiated brand allegiance is high, since the value of product comparison at the moment of transaction is fairly low. The only differentiating factor then becomes supply liquidity which gives the larger competitor a big advantage

Ride-sharing in multiple cities:

This is a fairly commoditized market where first movers advantage is real, given the brand loyalty discussed above

“Winning” other cities helps tilt the scale in your favor when trying to win a new city, through global/national brand awareness and (frequent business) travelers that are already loyal to your brand.

Tipping points: Major expansion of the market potential can be caused by:

Getting and keeping supply liquidity at a high point when ride-sharing starts becoming the default choice for other transportation needs

Transporting not just people

Parting thoughts:

I think network effects do exist within each side of the market, but perhaps not according to the formal definition that conforms to Metcalfe’s Law: in places with dense social networks (like cities) there’s definitely a viral component to building brand loyalty and both companies are incentivising existing riders to get new riders to start using the service. Furthermore, more recently both companies started experimenting with products that benefit from more conventional network effects (Uber Pool / Lyft Line).

I share Ben’s concerns about Uber’s perceived lack of ethics especially in light of the monopolistic dynamics in the market that it’s in. Such dynamics is a textbook example of market failure, and in my mind the only real and valid reason why some form of regulation is required here. So far, it seems like most regulatory efforts have been centered around preventing ride-sharing services from disrupting existing incumbents, which feels like the wrong regulatory objective to optimize for.

Some, but not all, of the patterns discussed above, exist in other “sharing economy” marketplaces as well, and can potentially lead to similar outcomes and risks.

The gist: a few key principles (no one is ever happy with comp, and comp never made anyone happy; salary opacity is a myth; etc.) yield a fairly simple, transparent and straight-forward comp system.

If you acknowledge the limited (but still critical) role that comp plays in attracting and retaining the best talent, and that fairly quickly, group effects (like fairness) become much more important than individual effects (attracting/retaining a specific person) – a very strong case can be made for a system that’s as formulaic as possible, and intentionally trying to keep discretion to the bare minimum.

Graham proposes a straw man in which a set of levels are applied cross-functionally, and base comp and equity are set only according to them. The only functional exception is sales, but comp for those roles is being addressed with the same formulaic rigor. There are no exceptions and no negotiations. The only discretion that a hiring manager may have is around slotting a candidate/employee into a certain level.

My favorite part in the article is its end, where an outline for scaling the plan as the company grows is discussed. This is an often overlooked aspect of such systems and I was delighted to see that it wasn’t ignored in this piece.

On a slightly more philosophical/abstract level, it’s interesting to think about the role equity typically plays in this plan and others. I think most startups view equity as a necessity for keeping cash burn rates under control, as well as a self-selection mechanism for attracting talent with high risk-tolerance w/r/t their personal comp (which is viewed as a desirable trait). A scenario in which one or more of these constraints/assumptions is relaxed, or a scenario in which they are supplemented with add’l assumptions about the purpose of equity, may suggest a different role for equity in such system.

The gist is very simple, in Fred’s own words: “This story is designed to illustrate the fact that software alone is a commodity. There is nothing stopping anyone from copying the feature set, making it better, cheaper, and faster. And they will do that.”

In particular, Fred is making a case for software companies who are taking advantage of a network effect to create a defensible competitive advantage.

When people talk about utilizing a SaaS (Software-as-a-Service) business model, they often refer to the revenue model (subscription) and delivery model (cloud vs. on-prem). But if you’re only thinking about these two aspects, you’re not building SaaS, you’re building SaaC – Software-as-a-Commodity.

Depending on the problem your product is meant to solve, a SaaS business model can put you in a better position to pull certain defensible differentiation levers and not others. But either way, those levers are not going to pull themselves…